The wind turbine is a tool used to convert wind energy into electrical energy. This research applies the maximum power point tracking (MPPT) algorithm combined with the fuzzy sliding mode control (FSMC) to produce maximum power in the wind turbine. Addition of fuzzy logic algorithm to sliding mode control to reduce the chattering phenomenon caused by the high switching frequency of the MOSFET in the boost converter. The permanent magnet synchronous generator (PMSG) type of generator with a capacity of 600 watts is used to convert the mechanical energy of the turbine into electrical energy. Tracing the maximum power value of the generator with the MPPT-FSMC algorithm in this study based on the value of the generator output voltage, generator output current, and converter output voltage obtained through simulations on MATLAB / SIMULINK. Comparison of wind turbine performance using MPPT-FSMC and without MPPT is shown as validation of improved wind turbine performance when using intelligent control algorithm.

Currently, biogas as an alternative fuel has been widely used in the community, including in lamps and biogas stoves. There has been a surplus of biogas in some production regions due to a relatively small need for biogas fuel. So that most biogas users utilize the surplus to become generator fuel. Yet, in the application there is a drawback, namely the instability of the electric power generated per unit time. This is caused by not achieving the optimal water-fuel ratio because the volume of biogas production from the reactor is fluctuating based on the volume of raw material, such as processed cow dung. Therefore, a control method using a PID Controller is constructed to determine the best value of the AFR on a dual fuel generator. The objective is to generate an optimal output of electric power. The generator set is a tool used to generate energy or electrical power. The electric power generated by the generator set used to supply the electrical loads in this research are lamps. Power produced by a generator set ranges from 100 to 1200 watts. The power generated by the generator set is affected by a mixture of air and fuel. The generator set is dual fuel. From the results of this study, a stable response with an overshoot value is below 0% and its error is 2%. In addition, the best-obtained value of the AFR is 15.06. Furthermore, the stability of the power generated by the generator set is also influenced by the flow rate mass of the fuel injected into the combustion chamber. From the simulation results, when given a power set point at 1200 watts, the obtained value of the air mass flow rate is 0.03754 kg/s, the mass flow rate of biogas is 0.002367 kg/s, and the gasoline constant mass flow rate is 0.000125 kg/s. Meanwhile, when given a set point of 100 watts and a value of 0.009928 kg/s air mass flow rate is injected into the chamber, the mass flow rate of biogas is 0.0005341 kg/s, and the mass flow rate of gasoline is 0.000125 kg/s. In this research, the value of AFR for complete combustion on a dual fuel system is 15.06. The results have shown that the PID Controller has been successfully implemented to regulate AFR, and the generator output of power can be constant.

Generator set is a tool that is used to generate energy or electrical power. The electric power generated by the generator set is used to supply the electrical loads in this thesis is lamp. Power that can be produced by a generator set is ranged between 100-1200 watt. The power generated by generator set is used influenced by a mixture ofair and fuel. Generator set used is dual fuel generator set. From the results of this study, a stable response with overshoot value below 0% and its error is 2%. In addition, the best obtained value of Air Fuel Ratio (AFR) is 15.06, Furthermore, the stability of the power generated by the generator set is also influenced by the flowrate mass of the fuel injected into the combustion chamber. From the simulation results when given a power set point at 1200 watt, obtained value of the air mass flowrate is 0.03754 kg/s, mass flowrate of biogas is 0.002367 kg/s, and gasoline constant mass flowrate is 0.000125 kg/s. Meanwhile, when given a set point of 100 watts of power, a value of 0.009928 kg/s air mass flowrate is injected to the chamber, mass flowrate of biogas is 0.0005341 kg/s and the mass flowrate of gasoline is 0.000125 kg/s. The goal of output power control on generator set is to make generator working to produce output power in accordance to required load.

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AbstrakAbstrak -Saat ini konsumsi energi di Indonesia mengalami peningkatan sehingga pemanfaatan energi terbarukan lebih dikembangkan untuk memenuhi proyeksi kebutuhan energi masa depan. Salah satu sumber energi terbarukan yang sedang dikembangkan penggunaannya adalah biogas, khususnya untuk biogas skala rumah tangga. Implementasi biogas pada skala rumah tangga ada beberapa macam, salah satunya adalah penggunaan biogas sebagai bahan bakar generator untuk menghasilkan listrik. Bahan bakar generator bisa menggunakan biogas secara penuh atau bahan bakar campuran gasoline dengan biogas. Generator set listrik dengan bahan bakar ganda gasoline-biogas dapat menghemat penggunaan gasoline sebagai bahan bakar dan juga dapat menigkatkan performansi generator. Rasio campuran gasoline-biogas berpengaruh terhadap performansi engine, salah satunya pada kecepatan putar. Namun saat ini rasio campuran gasoline dengan biogas masih diatur secara manual pada penggunaan biogas skala rumah tangga. Berdasarkan pada kondisi tersebut, maka dalam penelitian ini dikembangkan metode jaringan syaraf tiruan / artificial neural networks (ANN) yang bertujuan untuk mencari rasio optimal agar mendapatkan karakterisasi kecepatan putar generator set dengan nilai performansi engine terbaik. Sebanyak 300 variasi data diolah menggunakan JST 75% untuk training dengan jumlah hidden node 100 nilai net.trainParam.goal = 0.0001, net.trainParam.lr = 0.01, dan net.trainParam.epochs = 1000, serta 25% untuk uji. Penelitian ini menghasilkan nilai RMSE training sebesar 10,4812 pada node ke 55 dan nilai RMSE uji sebesar 5,8301 dengan hasil kecepatan putar 3445,87, dan mendapatkan rasio terbaik pada gasoline 0,012 L/menit dan biogas 5 L/menit. AbstractAbstract--Currently, energy consumption in Indonesia has increased so that the utilization of renewable energy is more developed to supply projections for future energy needs. One of the renewable energy sources that is being developed is biogas, especially for household-scale biogas. There are several types of biogas implementation at the household scale, one of which is the use of biogas as generator fuel to produce electricity. Fuel generators can use biogas in full or mix gasoline with biogas fuel. Electric generator sets with dual gasoline-biogas fuel can save the use of gasoline as fuel and can also increase the performance of generators. The gasoline-biogas mixture ratio affects engine performance, one of which is the rotational speed. However, at present the ratio of gasoline to biogas is still manually regulated on household scale biogas usage. Based on these conditions, the artificial neural networks (ANN) method was developed in this study which aims to find the optimal ratio in order to get the generator set rotational speed characterization with the best engine performance value. A total of 300 variations of data were processed using 75% for training with the number of hidden nodes 100 net.trainParam.goal value = 0.0001, net.trainParam.lr = 0.01, and net.trainParam.epochs =